Current weather prediction and climate modeling systems, now largely computer-based, rely on measuring changes in atmospheric pressure, temperature, and humidity over time. By monitoring changes in these parameters over an extended period the trends can be used by computer models to make predictions regarding the short term individual weather events and patterns of a few days or so. Longer timescale predictions are typically made possible by aspects of the climate system which vary on timescales which are longer than individual weather events. For example, the El Nino Southern Oscillation (ENSO) varies on timescales from seasons to years and involves atmospheric changes over the earth's and ocean's surface.
Some current methods for forecasting use coupled atmosphere-ocean global circulation computer models (AOGCM) that solve the physical equations of the system and represent the complex interactions between all aspects of the climate system. Generally, observations or measurements of the current state of the climate system are acquired, and these are input into the model to produce a best estimate of its current state. The model is then run forward in time to produce the forecast. Rather than running the model once, from a single initial state, a range of different initial states is used so that a number of forecasts are produced which are hoped to span the range of future weather states consistent with current information.
These models typically use atmospheric measurements including those obtained from satellite imagery, weather balloons, weather stations, and visual confirmation. Satellites orbit thousands of miles above the earth, weather balloons are infrequent and subject to random movement, weather stations are spread sometimes miles apart, and visual confirmation can only inform that the weather being reported is, in fact occurring.
Accordingly, there is a need for improved climate modeling and weather forecasting systems and methods.